Kathy Applebaum

2. Practical Uses of Machine Learning and Ignition Kathy Applebaum Co-Director of Sales Engineering / Inductive Automation Kevin McClusky Senior Software Engineer / Inductive Automation

3. Machine Learning Practical uses of machine learning What is machine learning Before you start Steps to a machine learning project Conclusion Resources to learn more Agenda

4. Practical Uses Predictive maintenance Quality control Demand forecasting Training industrial robots Self-driving vehicles Machine Learning in Industrial Automation

5. Implementation Options More Automated More Control

6. Implementation Options More Automated More Control

7. What is Machine Learning? Analytics Machine Learning Artificial Intelligence Three main branches

8. What is Machine Learning? Analytics • Knowledge discovery Three main branches

9. What is Machine Learning? Analytics • Knowledge discovery • Descriptive Three main branches

10. What is Machine Learning? Analytics • Knowledge discovery • Descriptive • Diagnostic Three main branches

11. What is Machine Learning? Analytics • Knowledge discovery • Descriptive • Diagnostic • Predictive Three main branches

12. What is Machine Learning? Analytics • Knowledge discovery • Descriptive • Diagnostic • Predictive • Prescriptive Three main branches

13. What is Machine Learning? Analytics • Knowledge discovery Machine Learning • Learn and improve from experience Three main branches

14. What is Machine Learning? Analytics • Knowledge discovery Machine Learning • Learn and improve from experience Artificial Intelligence • Tasks that simulate human intelligence Three main branches

15. What is Machine Learning? Demo

16. What is Machine Learning? Classifiers – predict a category Regression – predict a value Main types of machine learning

17. What is Machine Learning? Algorithms

18. What is Machine Learning? K-means - Clustering

19. What is Machine Learning? K-means - Clustering Categorization for Defect Analysis

20. What is Machine Learning? Decision trees Should I bring an umbrella? Cloudy? Yes Rain in forecast? Yes No

21. What is Machine Learning? Decision trees Predictive Maintenance Should I bring an umbrella? Cloudy? Yes Rain in forecast? Yes No

22. What is Machine Learning? Regression analysis

23. What is Machine Learning? Regression analysis Process Tuning Production Forecasting

24. What is Machine Learning? Neural Networks

25. What is Machine Learning? Neural Networks Process Simplification Vision Systems

26. Getting Started What things should we have before starting a machine learning project? Prerequisites

27. Getting Started Data Prerequisites

28. Getting Started Data • Sources of data Prerequisites

29. Getting Started Data • Sources of data • Quality data Prerequisites

30. Getting Started Data • Sources of data • Quality data • Labeled vs. unlabeled data Prerequisites

31. Getting Started Data Statistics knowledge Prerequisites

32. Getting Started Data Statistics knowledge • Sampling techniques Prerequisites

33. Getting Started Data Statistics knowledge • Sampling techniques • Correlation vs. causation Prerequisites

34. Getting Started Data Statistics knowledge • Sampling techniques • Correlation vs. causation • How good are your results? Prerequisites

35. Getting Started Data Statistics knowledge Domain knowledge Prerequisites

36. Getting Started Data Statistics knowledge Domain knowledge • In-depth knowledge about your process Prerequisites

37. Getting Started Data Statistics knowledge Domain knowledge • In-depth knowledge about your process • Know what types of data are promising Prerequisites

38. Getting Started Data Statistics knowledge Domain knowledge • In-depth knowledge about your process • Know what types of data are promising • Know when results don’t make sense Prerequisites

39. Machine Learning Steps Pick a question to answer

40. Machine Learning Steps Pick a question to answer • High value vs. easy

41. Machine Learning Steps Pick a question to answer • High value vs. easy • Cost function

42. Machine Learning Steps Pick a question to answer Use domain knowledge

43. Machine Learning Steps Pick a question to answer Use domain knowledge • Pick useful data

44. Machine Learning Steps Pick a question to answer Use domain knowledge • Pick useful data • Acquire missing data

45. Machine Learning Steps Pick a question to answer Use domain knowledge • Pick useful data • Acquire missing data • Quality data

46. Machine Learning Steps Pick a question to answer Use domain knowledge • Pick useful data • Acquire missing data • Quality data • Dependent variables

47. Machine Learning Steps Pick a question to answer Use domain knowledge ETL

48. Machine Learning Steps Pick a question to answer Use domain knowledge ETL • Automate steps

49. Machine Learning Steps Pick a question to answer Use domain knowledge ETL • Automate steps • Acquire new data automatically

50. Machine Learning Steps Pick a question to answer Use domain knowledge ETL • Automate steps • Acquire new data automatically • Clean up data